以下文章来源于明亮公司 ，作者主编24小时在线 本文来自微信公众号： 明亮公司 ，作者：MD，原文标题：《黄仁勋最新访谈：英伟达投资OpenAI 不是签署大额订单的前提》，题图来自：视觉中国 近期，英伟达 （NVDA.US） 投资"出手"频繁，先是宣布50亿美元投资英特尔，随后斥资至多1000 亿美元投资OpenAI，而受此前OpenAI与甲骨文的合作，市场均在股价层面给予了积极反馈。 但市场也出现了质疑声音——称英伟达、OpenAI与甲骨文存在"收入循环"，财务数字"操作"大于实 际营收。 9月25日，在播客BG2最新一期节目中，BG2主播、Altimeter Capital创始人Brad Gerstner， Altimeter Capital合伙人Clark Tang与英伟达CEO黄仁勋展开了一次对话。黄仁勋在对话中回应了当 下市场关心的问题。 黄仁勋认为，投资OpenAI实际上是一个很好的机会，并认为OpenAI将是下一家数万亿美元级别的 Hyperscaler。 此外，黄仁勋也特别解释了为什么ASIC芯片并不完全和英伟达GPU是竞争关系——因为英伟达是AI 基础设施提供商，其提供的能力范围已经不仅仅是 ...

公众号记得加星标⭐️，第一时间看推送不会错过。 编者按： 随着芯片制造工艺来到了2nm后，GAA晶体管开始逐渐进入主流。到翻看这个技术的发 展，最早在2006年就有相关研究发布。当中论文的参与者还有一个华人。 在本文中，我们回顾一下20年前是如何看待这个晶体管的。 早期研究展示了下一代晶体管设计的新方法 随着微电子行业开始在下一代智能手机中采用环栅晶体管设计，劳伦斯伯克利国家实验室（伯克利 实验室）近 20 年前的开创性研究展示了一种创建这些先进结构的创新方法。 这项名为"环栅场效应晶体管"（GAA-FET）的技术代表着一项关键的架构进步，有望将数十亿个 晶体管封装到智能手机和笔记本电脑的微型芯片中。"环栅"设计增强了对晶体管沟道的控制，从而 提高了性能并降低了功耗。虽然目前业界仍在通过传统的自上而下的制造方式来实现GAA-FET， 但伯克利实验室早期的自下而上方法展示了这种几何结构利用化学合成实现这些复杂结构的潜力。 图示：在环栅 (GAA) 结构（右图）中，栅极环绕纳米级硅通道的四边，纳米级硅通道以三条灰色纳米线 与黄金矩形相交的形式呈现。这些通道是电流的通道。在鳍式场效应晶体管 (FinFET) 结构（ ...

153 虽然黄仁勋本人只是轻描淡写地用一句"英伟达很高兴成为英特尔的股东"来概括这一事件，但只要是对 芯片行业有所关注的人都知道，这次芯片界两大巨擘在历经三十年恩怨情仇后的惊人握手，绝不会只是 一场简简单单的交易。它不仅会震动华尔街的股市曲线，更会在全球科技圈掀起巨大的波澜。 英特尔和英伟达，一个在PC时代铸就了庞大的帝国，将"Intel In-side"的印记深深植入亿万用户的内 心；另一个则在像素与帧数的洪流中崛起，用精湛的图形处理技术点亮了虚拟世界。为了争夺芯片市场 的主导权，它们曾互为敌手二十余年。如今它们放下恩怨，很可能会带来整个芯片行业的一场重大洗 牌。在这个节点上，重新回望双"英"之间多年的博弈，或许可以帮助我们更好地看懂芯片行业的过去、 现在和未来。 陈永伟 北京时间9月19日凌晨，英伟达创始人黄仁勋与英特尔首席执行官陈立武共同召开了一场线上发布会。 会上，黄仁勋宣布了一个爆炸性的消息：英伟达将向老对手英特尔注资50亿美元，并与其携手开发革命 性的"Intel x86 with RTX"芯片。 一、起初的岁月静好 1992年底，加州圣何塞市丹尼餐厅的服务生发现，有三位年轻工程师三天两头来店里。 ...

公众号记得加星标⭐️，第一时间看推送不会错过。 来源 ： 内容 编译自 semiwiki 。 在日前的TSMC OIP生态论坛上，台积公司资深院士兼研发/设计与技术平台副总裁Dr. LC Lu在一个 演讲中指出，人工智能的普及推动了电力需求的指数级增长。从超大规模数据中心到边缘设备，人工 智能正渗透到各个领域，为日常生活中的各种新应用注入新的活力。 这些不断发展的模型，包括具身人工智能、思维链推理和代理系统，需要更大的数据集、更复杂的计 算和更长的处理时间。这种激增导致人工智能加速器在五年内每封装功耗增加了3倍，部署规模在三 年内增加了8倍，因此能源效率对于人工智能的可持续增长至关重要。 对此，台积电把战略重点放在先进逻辑和3D封装创新，并结合生态系统协作，以应对这一挑战。从 逻辑微缩开始，台积电的路线图非常稳健：N2将于2025年下半年投入量产，N2P计划于明年投入量 产，A16将于2026年底实现背面供电，A14则进展顺利。 至于N3 和 N5 的增强功能持续提升价值。从 N7 到 A14，等功率下的速度提升了 1.8 倍，而功率效 率提升了 4.2 倍，每个节点的功耗比上一代降低约 30%。A16 的后 ...

如果您希望可以时常见面，欢迎标星收藏哦~ 来源：内容来自半导体芯闻综合。 市场传出，台积电相当于1.4纳米的「A14」制程的良率进展已经超前。根据The Futurum Group 半 导 体 分 析 师 Ray Wang 透 过 社 交 平 台 X 公 布 的 讯 息 ， 台 积 电 A14 制 程 的 「 良 率 表 现 」 (yield performance)进展已经超前。 根 据 Wang 提 供 的 讯 息 ， 台 积 电 A16 制 程 整 合 了 片 电 晶 体 、 超 级 电 轨 (SPR) 及 创 新 的 背 面 接 面 (backside contact)设计，相较于N2P制程，A16的速度提升8~10%、功耗降低15~20%，芯片密度 增加约1.1倍，非常适合用于需要复杂讯号传输、稳定供电的高效能运算(HPC)产品。 相较之下，A14完整接续N2制程，专为AI及智能型手机应用量身打造，具备进阶的NanoFlex Pro 单元架构。 吴诚文解释，一是过去台湾半导体产业多仰赖国外，但现在晶圆制造、封测连结本地自有供应链比 重增加，尤其三大园区有些厂商过去不在供应链中，但半导体业发展快速、且 ...

Group 1 - Baidu's search engine is undergoing a significant transformation towards AI integration, referred to internally as "Big Search," marking the largest change in a decade [1] - The AI-driven agent model is expected to assist users in completing tasks beyond traditional keyword searches, indicating a shift in user interaction [1] - Baidu's Wenku and cloud storage services are also expanding, aiming to create a "one-stop AI creation platform" with a dedicated team of 1,200 [1] Group 2 - The article discusses the evolution of the internet ecosystem, highlighting the complexity of user needs and the competitive landscape dominated by major players like BAT and FANG [2] - The historical context of the internet's development is explored, noting the transition from information-centric models to more integrated social and e-commerce platforms [3] Group 3 - The recommendation engine developed by Baidu is based on user behavior data, aiming to enhance targeted advertising through detailed user profiling [5] - The article critiques the current state of content production, suggesting that the focus on quantity over quality has led to a decline in meaningful engagement [6] Group 4 - The dominance of algorithm-driven content distribution is noted, with implications for user experience and the overall information ecosystem [8] - Baidu's market position is analyzed in light of competition from ByteDance, emphasizing the challenges faced by traditional search models in adapting to new content consumption patterns [8] Group 5 - The article reflects on the missed opportunities for Baidu in the early days of algorithm distribution, suggesting that a more proactive approach could have altered its competitive stance [11] - The potential of AI to revolutionize information access and user interaction is highlighted, with a focus on the implications for Baidu's future strategies [19][20] Group 6 - Baidu's early commitment to AI, including the establishment of a deep learning research institute, is acknowledged, though recent performance in AI competitions has raised questions about its strategic direction [20] - The article emphasizes the importance of application development in AI, suggesting that successful models will depend on practical use cases rather than theoretical frameworks [32]

Core Viewpoint - The semiconductor equipment industry is undergoing a significant transformation driven by differing technological perspectives among major players, with implications for growth and competition in the market [2][4][10]. Group 1: Company Perspectives - Applied Materials' CEO Gary Dickerson predicts "low single-digit growth" for the wafer fabrication equipment market, reflecting a cautious stance on the future of technology development, particularly in advanced packaging technology [4]. - KLA Corporation's CFO Bren Higgins anticipates "mid-single-digit growth," emphasizing the increasing importance of advanced process control and inspection technologies as semiconductor processes become more complex [5]. - Lam Research's CFO Doug Bettinger avoids numerical predictions, indicating a strategic flexibility as the company navigates multiple technology directions, including 3D NAND and advanced logic architectures [6]. Group 2: Market Dynamics - The semiconductor equipment industry is experiencing a shift from a purely technical competition to a complex competition that includes political risk management, influenced by geopolitical tensions and market restructuring [13]. - Applied Materials has seen its revenue from China plummet from 32% to 18%, losing not only income but also critical opportunities for technological development in the largest semiconductor market [8]. - KLA Corporation faces a $500 million loss, but the more significant concern is the potential fragmentation of global technology standards as Chinese fabs seek alternative solutions [9]. Group 3: Technological Challenges - AI chip manufacturing presents unprecedented challenges, requiring advanced integration techniques and stringent defect detection capabilities, which KLA is well-positioned to address with its advanced inspection technologies [11]. - Lam Research's focus on 3D architectures aims to reduce power consumption in AI model training, necessitating complex etching and deposition processes that push the boundaries of semiconductor manufacturing [12]. - The competition among these companies reflects their differing strategies: Applied Materials bets on packaging technology, KLA on the growing need for inspection, and Lam Research on maintaining strategic options [13].

Core Insights - The article discusses the current state of end-to-end (E2E) technology in both academia and industry, highlighting the differences in approach and data availability between the two sectors [1][4][5] - It emphasizes the importance of data iteration speed in the AI model development process, suggesting that a slow data iteration can hinder technological advancements [2][4] - The article also explores the role of reinforcement learning in enhancing Vision-Language Models (VLA), particularly in scenarios where there are no definitive correct answers [6][7][9][10] Summary by Sections End-to-End Technology - The academic field is experiencing a proliferation of end-to-end methodologies, with various approaches emerging [1] - In contrast, the industrial sector is more pragmatic, facing computational limitations that exclude some popular models, but benefiting from vast amounts of data [4] - The success of models like ChatGPT is attributed to the internet's ability to provide extensive data, which is also true for the automotive industry where companies can easily gather massive driving data [4] Data and Technology Iteration - The article stresses that as technology evolves rapidly, the iteration of datasets must keep pace; otherwise, it will impede technological progress [2] - Research teams are increasingly publishing datasets alongside their papers to maintain high-impact outputs [3] Reinforcement Learning and VLA - Reinforcement learning is suitable for problems where there are no correct answers, only characteristics of correct and incorrect answers [7] - The training process in reinforcement learning allows for the identification of optimal solutions based on reward systems, thus reducing the need for extensive demonstration data [9] - The article notes that while short-term results of VLA applications may be uncertain, the long-term potential is widely recognized [10][11] Future of VLA - The article suggests that the importance of algorithms in VLA models extends beyond mere performance metrics; factors such as data availability and training strategies are crucial [12] - The community is encouraged to engage in discussions about the development and challenges of autonomous driving technologies [5][13][16]

Core Insights - TSMC is a dominant player in the semiconductor industry, manufacturing approximately 60% of global foundry revenue and 90% of advanced node chips, positioning itself as a critical company in the technology sector [2] Historical Background - Morris Chang, after a successful career at Texas Instruments, founded TSMC in 1987 with the vision of creating a pure-play foundry that only manufactures chips without designing them, which was a novel approach at the time [4][5] - The foundry model significantly lowered startup costs for fabless design firms, allowing them to focus on chip design while TSMC handled manufacturing [7] Business Model and Strategies - TSMC's commitment to advancing technology has kept it 3-4 years ahead of competitors, with plans to produce 2nm nodes by 2025-2026 [9] - The company utilizes advanced technologies such as EUV lithography, which allows for the production of smaller transistors, essential for adhering to Moore's law [10][12] - TSMC maintains a non-competitive relationship with its customers, treating them as partners, which fosters collaboration and shared success [15][20] Technological Advancements - The introduction of EUV lithography by ASML has been pivotal for TSMC, enabling the production of smaller and more efficient chips [12][14] - TSMC's strategic partnerships, including co-investments with key suppliers like ASML, have aligned incentives and ensured shared technology roadmaps [17] Future Outlook - The geopolitical landscape, particularly U.S.-China relations, poses risks to TSMC's operations, as any disruption in Taiwan's chip industry could have significant global economic repercussions [21][22] - China is investing heavily in its semiconductor industry, aiming to dominate the supply chain by 2030, which could challenge TSMC's market position [23] - Despite global efforts to enhance domestic chip manufacturing, replicating TSMC's effectiveness and expertise remains a significant challenge [24]

Core Insights - The article discusses how entrepreneurs can leverage exponential forces and build network effects to create lasting value in the tech industry [3][4][5] Group 1: The Power of Networks and Network Effects - Many significant internet services are networks that become more valuable as more people use them, exemplified by email and social media platforms like Facebook and Instagram [5][6] - The tech industry benefits from powerful exponential forces, such as Moore's Law, which states that semiconductor performance doubles approximately every two years, leading to rapid advancements [6][7] - Entrepreneurs should focus on identifying these exponential forces, as they will dominate any tactical product work [6][10] Group 2: Strategies for Building Networks - Successful companies often start with a strong product that attracts users, then leverage existing networks to grow, as seen with Instagram and Substack [10][11] - The challenge lies in making networks useful from the beginning, as initial user bases can be small and unappealing [12] - The emergence of "narrow startups" that charge premium prices for specialized services indicates a shift towards more focused business models in the tech landscape [23] Group 3: The Role of Branding and Pricing - Brand power and consumer inertia are significant in the tech sector, as seen with ChatGPT's rapid rise to prominence despite lacking traditional network effects [15][21] - The increasing willingness of consumers to pay higher prices for software suggests a shift in spending priorities, with software potentially consuming a larger share of disposable income [14][21] Group 4: The Impact of AI and Open Source - The rise of AI tools has diminished the need for traditional web traffic, leading to a decline in SEO-driven traffic for many websites [20][21] - Open source software has played a crucial role in democratizing technology, allowing startups to thrive with minimal initial investment [35][36] - The future of open source AI remains uncertain, with potential for it to lag behind proprietary models, but it could provide affordable solutions for consumers [36][37]